{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style ='darkgrid', font_scale=1.5)  #设置背景\n",
    "plt.rcParams['font.sans-serif'] =['SimHei'] #设置字体，支持中文\n",
    "plt.rcParams['axes.unicode_minus'] =False\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:37:49.216504500Z",
     "start_time": "2024-05-07T08:37:49.205441400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1、数据查看"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import os"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:37:49.227689900Z",
     "start_time": "2024-05-07T08:37:49.217521300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "data_user = pd.read_csv('tianchi_mobile_recommend_train_user.csv',dtype = str)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:04.239454100Z",
     "start_time": "2024-05-07T08:37:49.230560Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(12256906, 6)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:04.297632600Z",
     "start_time": "2024-05-07T08:38:04.258773700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12256906 entries, 0 to 12256905\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   user_id        object\n",
      " 1   item_id        object\n",
      " 2   behavior_type  object\n",
      " 3   user_geohash   object\n",
      " 4   item_category  object\n",
      " 5   time           object\n",
      "dtypes: object(6)\n",
      "memory usage: 561.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_user.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:04.413294Z",
     "start_time": "2024-05-07T08:38:04.298221400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category           time\n0  98047837  232431562             1          NaN          4245  2014-12-06 02\n1  97726136  383583590             1          NaN          5894  2014-12-09 20\n2  98607707   64749712             1          NaN          2883  2014-12-18 11\n3  98662432  320593836             1      96nn52n          6562  2014-12-06 10\n4  98145908  290208520             1          NaN         13926  2014-12-16 21",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:04.485459100Z",
     "start_time": "2024-05-07T08:38:04.398523300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "         user_id    item_id behavior_type user_geohash item_category  \\\ncount   12256906   12256906      12256906      3922082      12256906   \nunique     10000    2876947             4       575458          8916   \ntop     36233277  112921337             1      94ek6ke          1863   \nfreq       31030       1445      11550581         1052        393247   \n\n                 time  \ncount        12256906  \nunique            744  \ntop     2014-12-11 22  \nfreq            54797  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>3922082</td>\n      <td>12256906</td>\n      <td>12256906</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>10000</td>\n      <td>2876947</td>\n      <td>4</td>\n      <td>575458</td>\n      <td>8916</td>\n      <td>744</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>36233277</td>\n      <td>112921337</td>\n      <td>1</td>\n      <td>94ek6ke</td>\n      <td>1863</td>\n      <td>2014-12-11 22</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>31030</td>\n      <td>1445</td>\n      <td>11550581</td>\n      <td>1052</td>\n      <td>393247</td>\n      <td>54797</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:15.556825500Z",
     "start_time": "2024-05-07T08:38:04.448253200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "#把日期和小时分别取出来，从日期，小时两个角度分析购物行为\n",
    "data_user['date'] = data_user['time'].str[0:10]\n",
    "data_user['hour'] = data_user['time'].str[11:]  #如果发生溢出就分开执行\n",
    "\n",
    "#类型转换\n",
    "data_user['date'] = pd.to_datetime(data_user['date'])\n",
    "data_user['time']= pd.to_datetime(data_user['time'])\n",
    "data_user['hour'] = data_user['hour'].astype(int) #可以改为np.int8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:27.147755900Z",
     "start_time": "2024-05-07T08:38:15.960837400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "#对数据进行一下排序\n",
    "data_user.sort_values(by ='time',ascending=True,inplace = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:37.004735400Z",
     "start_time": "2024-05-07T08:38:27.155759100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n1505077    73462715  378485233             1          NaN          9130   \n8686537    36090137  236748115             1          NaN         10523   \n4035788    40459733  155218177             1          NaN          8561   \n10113411     814199  149808524             1          NaN          9053   \n2936757   113309982    5730861             1          NaN          3783   \n\n               time       date  hour  \n1505077  2014-11-18 2014-11-18     0  \n8686537  2014-11-18 2014-11-18     0  \n4035788  2014-11-18 2014-11-18     0  \n10113411 2014-11-18 2014-11-18     0  \n2936757  2014-11-18 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1505077</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8686537</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4035788</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10113411</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2936757</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:37.043486600Z",
     "start_time": "2024-05-07T08:38:37.011631100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n5241608   132653097  119946062             2          NaN          6054   \n10296029  130082553  296196819             1          NaN         11532   \n8527264    43592945  350594832             1      9rhhgph          9541   \n6263497    12833799  186993938             1      954g37v          3798   \n9200479    77522552   69292191             1          NaN           889   \n\n                        time       date  hour  \n5241608  2014-12-18 23:00:00 2014-12-18    23  \n10296029 2014-12-18 23:00:00 2014-12-18    23  \n8527264  2014-12-18 23:00:00 2014-12-18    23  \n6263497  2014-12-18 23:00:00 2014-12-18    23  \n9200479  2014-12-18 23:00:00 2014-12-18    23  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5241608</th>\n      <td>132653097</td>\n      <td>119946062</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>6054</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>10296029</th>\n      <td>130082553</td>\n      <td>296196819</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>11532</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>8527264</th>\n      <td>43592945</td>\n      <td>350594832</td>\n      <td>1</td>\n      <td>9rhhgph</td>\n      <td>9541</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>6263497</th>\n      <td>12833799</td>\n      <td>186993938</td>\n      <td>1</td>\n      <td>954g37v</td>\n      <td>3798</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>9200479</th>\n      <td>77522552</td>\n      <td>69292191</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>889</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:37.055903200Z",
     "start_time": "2024-05-07T08:38:37.043151600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "#drop=True，代表丢弃原有索引，按位置重新生成索引，在原有df生效\n",
    "data_user.reset_index(drop =True,inplace =True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:37.104202400Z",
     "start_time": "2024-05-07T08:38:37.055903200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id    item_id behavior_type user_geohash item_category       time  \\\n0   73462715  378485233             1          NaN          9130 2014-11-18   \n1   36090137  236748115             1          NaN         10523 2014-11-18   \n2   40459733  155218177             1          NaN          8561 2014-11-18   \n3     814199  149808524             1          NaN          9053 2014-11-18   \n4  113309982    5730861             1          NaN          3783 2014-11-18   \n\n        date  hour  \n0 2014-11-18     0  \n1 2014-11-18     0  \n2 2014-11-18     0  \n3 2014-11-18     0  \n4 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:37.182045100Z",
     "start_time": "2024-05-07T08:38:37.078787400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n12256901  132653097  119946062             2          NaN          6054   \n12256902  130082553  296196819             1          NaN         11532   \n12256903   43592945  350594832             1      9rhhgph          9541   \n12256904   12833799  186993938             1      954g37v          3798   \n12256905   77522552   69292191             1          NaN           889   \n\n                        time       date  hour  \n12256901 2014-12-18 23:00:00 2014-12-18    23  \n12256902 2014-12-18 23:00:00 2014-12-18    23  \n12256903 2014-12-18 23:00:00 2014-12-18    23  \n12256904 2014-12-18 23:00:00 2014-12-18    23  \n12256905 2014-12-18 23:00:00 2014-12-18    23  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12256901</th>\n      <td>132653097</td>\n      <td>119946062</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>6054</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12256902</th>\n      <td>130082553</td>\n      <td>296196819</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>11532</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12256903</th>\n      <td>43592945</td>\n      <td>350594832</td>\n      <td>1</td>\n      <td>9rhhgph</td>\n      <td>9541</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12256904</th>\n      <td>12833799</td>\n      <td>186993938</td>\n      <td>1</td>\n      <td>954g37v</td>\n      <td>3798</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12256905</th>\n      <td>77522552</td>\n      <td>69292191</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>889</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:37.182045100Z",
     "start_time": "2024-05-07T08:38:37.086895Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 日ARPU--按照每一天的方式去计算ARPU"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "data_user['operation']=1  #增加一列方便操作的作用\n",
    "data_user_buy2 = data_user.groupby(['date' , 'user_id' , 'behavior_type'])['operation'].count()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:40.408776700Z",
     "start_time": "2024-05-07T08:38:37.104202400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "date        user_id    behavior_type\n2014-11-18  100001878  1                127\n                       3                  8\n                       4                  1\n            100014060  1                 23\n                       3                  2\n                       4                  2\n            100024529  1                185\n                       3                 12\n                       4                  6\n            100027681  1                141\n                       3                  1\n                       4                  3\n            100042340  1                 33\n            10004287   1                115\n                       2                  5\n                       3                  4\n                       4                  2\n            100067745  1                 34\n            100078685  1                109\n                       3                  3\n            100086267  1                104\n                       3                  8\n            100097997  1                 30\n            100109755  1                120\n                       2                  4\n            100112744  1                 21\n                       2                  1\n            100128093  1                 13\n                       4                  2\n            100159569  1                  2\nName: operation, dtype: int64"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:40.454144900Z",
     "start_time": "2024-05-07T08:38:40.408776700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "             date    user_id behavior_type  total\n0      2014-11-18  100001878             1    127\n1      2014-11-18  100001878             3      8\n2      2014-11-18  100001878             4      1\n3      2014-11-18  100014060             1     23\n4      2014-11-18  100014060             3      2\n...           ...        ...           ...    ...\n375435 2014-12-18    9996155             3      2\n375436 2014-12-18   99963140             1     78\n375437 2014-12-18   99963140             3      1\n375438 2014-12-18   99968428             1      4\n375439 2014-12-18   99989881             1     11\n\n[375440 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>user_id</th>\n      <th>behavior_type</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>1</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>3</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>1</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>375435</th>\n      <td>2014-12-18</td>\n      <td>9996155</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>375436</th>\n      <td>2014-12-18</td>\n      <td>99963140</td>\n      <td>1</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>375437</th>\n      <td>2014-12-18</td>\n      <td>99963140</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>375438</th>\n      <td>2014-12-18</td>\n      <td>99968428</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>375439</th>\n      <td>2014-12-18</td>\n      <td>99989881</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n<p>375440 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2.reset_index().rename(columns ={'operation':'total'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:40.552448500Z",
     "start_time": "2024-05-07T08:38:40.440737Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "#上面是逐步解析，这里整体编写，二合1\n",
    "data_user_buy2 = data_user.groupby(['date' , 'user_id' , 'behavior_type'])['operation'].\\\n",
    "    count().reset_index().rename(columns ={'operation':'total'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:43.899626900Z",
     "start_time": "2024-05-07T08:38:40.556447300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "         date    user_id behavior_type  total\n0  2014-11-18  100001878             1    127\n1  2014-11-18  100001878             3      8\n2  2014-11-18  100001878             4      1\n3  2014-11-18  100014060             1     23\n4  2014-11-18  100014060             3      2\n5  2014-11-18  100014060             4      2\n6  2014-11-18  100024529             1    185\n7  2014-11-18  100024529             3     12\n8  2014-11-18  100024529             4      6\n9  2014-11-18  100027681             1    141\n10 2014-11-18  100027681             3      1\n11 2014-11-18  100027681             4      3\n12 2014-11-18  100042340             1     33\n13 2014-11-18   10004287             1    115\n14 2014-11-18   10004287             2      5\n15 2014-11-18   10004287             3      4\n16 2014-11-18   10004287             4      2\n17 2014-11-18  100067745             1     34\n18 2014-11-18  100078685             1    109\n19 2014-11-18  100078685             3      3\n20 2014-11-18  100086267             1    104\n21 2014-11-18  100086267             3      8\n22 2014-11-18  100097997             1     30\n23 2014-11-18  100109755             1    120\n24 2014-11-18  100109755             2      4\n25 2014-11-18  100112744             1     21\n26 2014-11-18  100112744             2      1\n27 2014-11-18  100128093             1     13\n28 2014-11-18  100128093             4      2\n29 2014-11-18  100159569             1      2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>user_id</th>\n      <th>behavior_type</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>1</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>3</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>1</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>4</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>1</td>\n      <td>185</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>3</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>4</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>1</td>\n      <td>141</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2014-11-18</td>\n      <td>100042340</td>\n      <td>1</td>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>1</td>\n      <td>115</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>4</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2014-11-18</td>\n      <td>100067745</td>\n      <td>1</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2014-11-18</td>\n      <td>100078685</td>\n      <td>1</td>\n      <td>109</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2014-11-18</td>\n      <td>100078685</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2014-11-18</td>\n      <td>100086267</td>\n      <td>1</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2014-11-18</td>\n      <td>100086267</td>\n      <td>3</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2014-11-18</td>\n      <td>100097997</td>\n      <td>1</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2014-11-18</td>\n      <td>100109755</td>\n      <td>1</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2014-11-18</td>\n      <td>100109755</td>\n      <td>2</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2014-11-18</td>\n      <td>100112744</td>\n      <td>1</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2014-11-18</td>\n      <td>100112744</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2014-11-18</td>\n      <td>100128093</td>\n      <td>1</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2014-11-18</td>\n      <td>100128093</td>\n      <td>4</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2014-11-18</td>\n      <td>100159569</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:43.990896Z",
     "start_time": "2024-05-07T08:38:43.902660Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    0.588050\n2014-11-19    0.574143\n2014-11-20    0.546660\n2014-11-21    0.481358\n2014-11-22    0.577016\n2014-11-23    0.525184\n2014-11-24    0.526025\n2014-11-25    0.545426\n2014-11-26    0.562058\n2014-11-27    0.577135\n2014-11-28    0.519955\n2014-11-29    0.515906\n2014-11-30    0.566860\n2014-12-01    0.597341\n2014-12-02    0.552824\n2014-12-03    0.589977\n2014-12-04    0.565151\n2014-12-05    0.521282\n2014-12-06    0.508075\n2014-12-07    0.507007\n2014-12-08    0.520871\n2014-12-09    0.525282\n2014-12-10    0.483464\n2014-12-11    0.467943\n2014-12-12    1.975518\n2014-12-13    0.513282\n2014-12-14    0.522346\n2014-12-15    0.554590\n2014-12-16    0.560410\n2014-12-17    0.544182\n2014-12-18    0.544819\ndtype: float64"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4代表支付\n",
    "#每天的活跃用户的平均消费次数\n",
    "data_user_buy2.groupby('date').apply(lambda x: x[x['behavior_type'] =='4' ].total.sum()/len(x.user_id.unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:44.211255600Z",
     "start_time": "2024-05-07T08:38:43.935057100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "0         127\n1           8\n2           1\n3          23\n4           2\n         ... \n375435      2\n375436     78\n375437      1\n375438      4\n375439     11\nName: total, Length: 375440, dtype: int64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.total代表取total那列,通过点去获取对应列的编写效率更高\n",
    "data_user_buy2.total"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:44.257813Z",
     "start_time": "2024-05-07T08:38:44.211789800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "data_user_buy2_plot=data_user_buy2.groupby('date').apply(lambda x: x[x.behavior_type =='4' ].total.sum()/len(x.user_id.unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:44.353253800Z",
     "start_time": "2024-05-07T08:38:44.227186200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2014-11-18', '2014-11-19', '2014-11-20', '2014-11-21',\n               '2014-11-22', '2014-11-23', '2014-11-24', '2014-11-25',\n               '2014-11-26', '2014-11-27', '2014-11-28', '2014-11-29',\n               '2014-11-30', '2014-12-01', '2014-12-02', '2014-12-03',\n               '2014-12-04', '2014-12-05', '2014-12-06', '2014-12-07',\n               '2014-12-08', '2014-12-09', '2014-12-10', '2014-12-11',\n               '2014-12-12', '2014-12-13', '2014-12-14', '2014-12-15',\n               '2014-12-16', '2014-12-17', '2014-12-18'],\n              dtype='datetime64[ns]', name='date', freq=None)"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2_plot.index"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:44.382358300Z",
     "start_time": "2024-05-07T08:38:44.353594700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data_user_buy2_plot 画折线图\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.plot(data_user_buy2_plot.index,data_user_buy2_plot.values)\n",
    "plt.xticks(data_user_buy2_plot.index[::3], data_user_buy2_plot.index[::3], rotation =45)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.096902700Z",
     "start_time": "2024-05-07T08:38:44.377750200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 日ARPPU"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "date             10000\nbehavior_type    10000\ntotal            10000\ndtype: int64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#本月总用户数\n",
    "data_user_buy2.groupby('user_id').count().count()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.304067400Z",
     "start_time": "2024-05-07T08:38:45.096902700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "date             8886\nbehavior_type    8886\ntotal            8886\ndtype: int64"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#本月发生支付行为的用户总数\n",
    "data_user_buy2[data_user_buy2['behavior_type'] =='4'].groupby('user_id').count().count()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.305067900Z",
     "start_time": "2024-05-07T08:38:45.197687800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    2.423652\n2014-11-19    2.439444\n2014-11-20    2.320375\n2014-11-21    2.271429\n2014-11-22    2.530120\n2014-11-23    2.330780\n2014-11-24    2.248031\n2014-11-25    2.313961\n2014-11-26    2.402824\n2014-11-27    2.403405\n2014-11-28    2.231623\n2014-11-29    2.331881\n2014-11-30    2.357236\n2014-12-01    2.359083\n2014-12-02    2.284543\n2014-12-03    2.289334\n2014-12-04    2.328707\n2014-12-05    2.223041\n2014-12-06    2.253444\n2014-12-07    2.320741\n2014-12-08    2.204384\n2014-12-09    2.413576\n2014-12-10    2.230236\n2014-12-11    2.226363\n2014-12-12    3.913523\n2014-12-13    2.245320\n2014-12-14    2.312749\n2014-12-15    2.313460\n2014-12-16    2.285455\n2014-12-17    2.302548\n2014-12-18    2.310567\ndtype: float64"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#日ARPPU\n",
    "data_user_buy2.groupby('date').apply(lambda x: x[x['behavior_type'] =='4' ].total.sum()/x[x['behavior_type'] =='4' ].total.count())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.428948600Z",
     "start_time": "2024-05-07T08:38:45.287477400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 每日付费率\n",
    "### 每天购物人数占总人数的比例，这里是count，不是sum"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "#付费率\n",
    "pay_rate = data_user_buy2.groupby('date').apply(lambda x: x[x.behavior_type =='4' ].total.count()/len(x.user_id.unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.599856300Z",
     "start_time": "2024-05-07T08:38:45.433038900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    0.242630\n2014-11-19    0.235358\n2014-11-20    0.235591\n2014-11-21    0.211918\n2014-11-22    0.228059\n2014-11-23    0.225326\n2014-11-24    0.233994\n2014-11-25    0.235711\n2014-11-26    0.233915\n2014-11-27    0.240132\n2014-11-28    0.232994\n2014-11-29    0.221240\n2014-11-30    0.240477\n2014-12-01    0.253209\n2014-12-02    0.241985\n2014-12-03    0.257707\n2014-12-04    0.242689\n2014-12-05    0.234490\n2014-12-06    0.225466\n2014-12-07    0.218468\n2014-12-08    0.236289\n2014-12-09    0.217636\n2014-12-10    0.216777\n2014-12-11    0.210183\n2014-12-12    0.504793\n2014-12-13    0.228601\n2014-12-14    0.225855\n2014-12-15    0.239723\n2014-12-16    0.245207\n2014-12-17    0.236339\n2014-12-18    0.235795\ndtype: float64"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_rate"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.617514400Z",
     "start_time": "2024-05-07T08:38:45.603717800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "0.24156630970869047"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_rate.mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.661898900Z",
     "start_time": "2024-05-07T08:38:45.620507400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "0.23279209516184718"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_rate[pay_rate != pay_rate['2014-12-12']].mean()  #去掉双十二的影响"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:45.662263300Z",
     "start_time": "2024-05-07T08:38:45.634049300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可以看出平时只有23%的用户有消费行为，但是双12有50%的用户有消费行为\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "pay_rate.plot()\n",
    "plt.xticks(pay_rate.index[::3], pay_rate.index[::3], rotation =45)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:46.155967300Z",
     "start_time": "2024-05-07T08:38:45.651247800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 购物是发生在什么时间，这里是按照小时来分析"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "# 每天的每小时内的购买次数\n",
    "data_user_buy3 = data_user[data_user['behavior_type'] == '4'].groupby([ 'date', 'hour'])['operation'].sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:47.343057800Z",
     "start_time": "2024-05-07T08:38:46.159092400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "date        hour\n2014-11-18  0       115\n            1        35\n            2        29\n            3        13\n            4         6\n                   ... \n2014-12-18  19      169\n            20      249\n            21      259\n            22      248\n            23      227\nName: operation, Length: 744, dtype: int64"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:47.356804300Z",
     "start_time": "2024-05-07T08:38:47.343851500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "data_user_buy4=data_user_buy3['2014-11-18']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:47.400071400Z",
     "start_time": "2024-05-07T08:38:47.359743700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0     115\n1      35\n2      29\n3      13\n4       6\n5      14\n6      24\n7      50\n8     124\n9     185\n10    215\n11    250\n12    221\n13    310\n14    207\n15    196\n16    206\n17    142\n18    170\n19    189\n20    221\n21    292\n22    305\n23    211\nName: operation, dtype: int64"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy4"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:47.401033700Z",
     "start_time": "2024-05-07T08:38:47.376615400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.xticks(range(0,24))\n",
    "plt.plot(data_user_buy4.index,data_user_buy4.values)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:48.133712400Z",
     "start_time": "2024-05-07T08:38:47.396308500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0     4845\n1     1703\n2      806\n3      504\n4      397\n5      476\n6     1023\n7     1938\n8     3586\n9     5707\n10    7317\n11    7086\n12    6956\n13    7717\n14    7207\n15    7312\n16    6930\n17    5298\n18    5140\n19    6352\n20    7872\n21    8829\n22    8845\n23    6359\nName: operation, dtype: int64"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每小时的购买次数\n",
    "data_user_buy3 = data_user[data_user['behavior_type'] == '4'].groupby(['hour'])['operation'].sum()\n",
    "data_user_buy3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:48.833183800Z",
     "start_time": "2024-05-07T08:38:48.740028300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.xticks(range(0,24))\n",
    "plt.plot(data_user_buy3.index,data_user_buy3.values)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:49.159532700Z",
     "start_time": "2024-05-07T08:38:48.838299700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "# 可以看出，30天内的情况和一天内的情况差不多，都是21、22点达到高峰"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:49.310720700Z",
     "start_time": "2024-05-07T08:38:49.161546500Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 复购行为分析\n",
    "### 30天内，有两天及以上购买，则算复购"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分组第一个用户的id：\n",
      "100001878\n",
      "--------------------------------------------------\n",
      "该用户这个月的购买情况：\n",
      "270407     2014-11-18\n",
      "2439076    2014-11-24\n",
      "2439090    2014-11-24\n",
      "2440428    2014-11-24\n",
      "2660355    2014-11-25\n",
      "2672617    2014-11-25\n",
      "3075867    2014-11-26\n",
      "3076142    2014-11-26\n",
      "3178225    2014-11-26\n",
      "3627084    2014-11-27\n",
      "4041992    2014-11-29\n",
      "4121098    2014-11-29\n",
      "5844240    2014-12-03\n",
      "5990081    2014-12-04\n",
      "6034724    2014-12-04\n",
      "6034730    2014-12-04\n",
      "6034745    2014-12-04\n",
      "7741727    2014-12-08\n",
      "7763671    2014-12-08\n",
      "7874796    2014-12-08\n",
      "8537843    2014-12-10\n",
      "8541997    2014-12-10\n",
      "8545496    2014-12-10\n",
      "9804567    2014-12-12\n",
      "9822831    2014-12-12\n",
      "9832820    2014-12-12\n",
      "9835545    2014-12-12\n",
      "10065248   2014-12-13\n",
      "10515816   2014-12-14\n",
      "10856624   2014-12-15\n",
      "10873197   2014-12-15\n",
      "11011329   2014-12-15\n",
      "12011660   2014-12-18\n",
      "12014976   2014-12-18\n",
      "12249127   2014-12-18\n",
      "12249379   2014-12-18\n",
      "Name: date, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "#辅助理解,270407是行索引号\n",
    "for i in data_user[data_user.behavior_type =='4' ].groupby('user_id')['date']:\n",
    "    print(f\"分组第一个用户的id：\\n{i[0]}\")\n",
    "    print(\"-\"*50)\n",
    "    print(f\"该用户这个月的购买情况：\\n{i[1]}\")\n",
    "    break"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:49.918061200Z",
     "start_time": "2024-05-07T08:38:49.800739200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "#用户当天购买多次，只算一次，使用len(x.unique())\n",
    "#用户一个月内有多少天进行了购物\n",
    "data_rebuy = data_user[data_user.behavior_type =='4' ].groupby('user_id')['date'].apply(lambda x: len(x.unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:51.166466400Z",
     "start_time": "2024-05-07T08:38:49.919434600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id\n100001878    15\n100011562     3\n100012968    11\n100014060    12\n100024529     9\n             ..\n99960313      5\n9996155       3\n99963140      8\n99968428      9\n99989881     13\nName: date, Length: 8886, dtype: int64"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rebuy  # 每个用户一个月总计有多少天购买（某天购买多次，只算一次）"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:51.183361300Z",
     "start_time": "2024-05-07T08:38:51.167815400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "count    8886.000000\nmean        5.536912\nstd         4.021963\nmin         1.000000\n25%         2.000000\n50%         5.000000\n75%         8.000000\nmax        30.000000\nName: date, dtype: float64"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rebuy.describe()  # 每个用户平均5.5天有购买"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:51.236125900Z",
     "start_time": "2024-05-07T08:38:51.184392400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8717083051991897"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把购买次数大于等于2的用户，除以所有购物用户\n",
    "data_rebuy[data_rebuy>=2].count()/data_rebuy.count()  #复购行为达到87%"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:51.237121900Z",
     "start_time": "2024-05-07T08:38:51.199776200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 复购时间间隔"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "0   2014-11-18\n1   2014-11-18\n2   2014-11-18\n3   2014-11-18\n4   2014-11-18\nName: date, dtype: datetime64[ns]"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.date.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:51.247095600Z",
     "start_time": "2024-05-07T08:38:51.227265Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "#pd支持[]去取某一列，也支持点取某一列\n",
    "data_day_buy = data_user[data_user.behavior_type == '4'].groupby('user_id').date.\\\n",
    "    apply(lambda x:x)   # 这里的apply没什么用，只是为了展示分组"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:52.421170300Z",
     "start_time": "2024-05-07T08:38:51.810365700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id            \n100001878  270407     2014-11-18\n           2439076    2014-11-24\n           2439090    2014-11-24\n           2440428    2014-11-24\n           2660355    2014-11-25\n                         ...    \n99989881   8203371    2014-12-09\n           9248497    2014-12-12\n           9249028    2014-12-12\n           10601909   2014-12-14\n           11085567   2014-12-15\nName: date, Length: 120205, dtype: datetime64[ns]"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day_buy    # 一个层级索引的Series"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:52.437187200Z",
     "start_time": "2024-05-07T08:38:52.423176700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 方法一"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "#groupby 的diff是拿每一行，减去前一行的值，分组内进行\n",
    "data_day_buy = data_user[data_user.behavior_type == '4'].groupby('user_id').date.\\\n",
    "    apply(lambda x:x.diff(1))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:54.766354400Z",
     "start_time": "2024-05-07T08:38:52.438473Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 方法二"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "# def calculate_time_diff(group):\n",
    "#     group['time_diff'] = group['date'].diff()\n",
    "#     return group\n",
    "# data_day_buy = data_user[data_user.behavior_type == '4'].groupby('user_id').apply(calculate_time_diff)['time_diff']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:54.783665300Z",
     "start_time": "2024-05-07T08:38:54.769475600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id            \n100001878  270407        NaT\n           2439076    6 days\n           2439090    0 days\n           2440428    0 days\n           2660355    1 days\n           2672617    0 days\n           3075867    1 days\n           3076142    0 days\n           3178225    0 days\n           3627084    1 days\n           4041992    2 days\n           4121098    0 days\n           5844240    4 days\n           5990081    1 days\n           6034724    0 days\n           6034730    0 days\n           6034745    0 days\n           7741727    4 days\n           7763671    0 days\n           7874796    0 days\n           8537843    2 days\n           8541997    0 days\n           8545496    0 days\n           9804567    2 days\n           9822831    0 days\n           9832820    0 days\n           9835545    0 days\n           10065248   1 days\n           10515816   1 days\n           10856624   1 days\n           10873197   0 days\n           11011329   0 days\n           12011660   3 days\n           12014976   0 days\n           12249127   0 days\n           12249379   0 days\n100011562  9456440       NaT\n           10528822   2 days\n           11223675   2 days\n100012968  941844        NaT\n           1416196    2 days\n           2842468    3 days\n           2844773    0 days\n           2844850    0 days\n           2943375    1 days\n           3885222    2 days\n           4012841    1 days\n           4223620    0 days\n           4767873    2 days\n           5740170    2 days\nName: date, dtype: timedelta64[ns]"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day_buy.head(50)  # 是一个层级索引的Series"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:54.852357900Z",
     "start_time": "2024-05-07T08:38:54.786786800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "data_day_buy = data_day_buy.dropna()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:54.859270700Z",
     "start_time": "2024-05-07T08:38:54.800801200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timedelta64[ns]\n"
     ]
    }
   ],
   "source": [
    "# timedelta64[ns] 是 pandas 和 NumPy 中用于表示时间差（即两个时间点之间的差异）的数据类型。\n",
    "# 这种类型的对象通常由 pandas 的 Timestamp 对象相减得到，或者直接使用 pandas 或 NumPy 的时间差构造函数创建。\n",
    "print(data_day_buy.dtype)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:54.859270700Z",
     "start_time": "2024-05-07T08:38:54.813135100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "data_day_buy = data_day_buy.map(lambda x: x.days)  # x是timedelta64[ns]属性，.days返回时间差"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:55.060663400Z",
     "start_time": "2024-05-07T08:38:54.827437100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "data_day_buy = data_day_buy[data_day_buy >= 0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:55.074795800Z",
     "start_time": "2024-05-07T08:38:55.061662700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "count    111319.000000\nmean          1.351692\nstd           2.934267\nmin           0.000000\n25%           0.000000\n50%           0.000000\n75%           1.000000\nmax          30.000000\nName: date, dtype: float64"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day_buy.describe() # 所有用户的平均复购时间间隔是1.35天"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:55.112352700Z",
     "start_time": "2024-05-07T08:38:55.078235Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id\n100001878    0.857143\n100011562    2.000000\n100012968    2.000000\n100014060    1.304348\n100024529    1.120000\n               ...   \n99960313     2.428571\n9996155      0.600000\n99963140     1.444444\n99968428     0.486486\n99989881     1.687500\nName: date, Length: 8148, dtype: float64"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个用户的平均复购间隔\n",
    "data_day_buy.groupby('user_id').mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:55.122837200Z",
     "start_time": "2024-05-07T08:38:55.091730500Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 漏斗分析\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [],
   "source": [
    "data_user_count = data_user.groupby('behavior_type').size()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:55.680156400Z",
     "start_time": "2024-05-07T08:38:55.120804600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "behavior_type\n1    11550581\n2      242556\n3      343564\n4      120205\ndtype: int64"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_count #四种行为的数量  点击、收藏、加入购物车和支付"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:55.696293900Z",
     "start_time": "2024-05-07T08:38:55.683564Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12256906\n"
     ]
    }
   ],
   "source": [
    "# 所有行为都算pv\n",
    "pv_all = data_user['user_id'].count()\n",
    "print(pv_all)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.094485500Z",
     "start_time": "2024-05-07T08:38:55.696293900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9423733036706"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总浏览量中有多少人没有去进行收藏，购物车，支付就离开了，这个就是流失率，94%只是看看就走了\n",
    "data_user_count[0]/pv_all"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.108646900Z",
     "start_time": "2024-05-07T08:38:56.096257500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9702556953628566"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有多少人点击了但是没有加入购物车\n",
    "(data_user_count[0] - data_user_count[2])/data_user_count[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.176836500Z",
     "start_time": "2024-05-07T08:38:56.108646900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "33.61988159411347"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 点击多少次才会加入购物车的比例\n",
    "data_user_count[0]/data_user_count[2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.185128400Z",
     "start_time": "2024-05-07T08:38:56.126325200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "0.29400053556251526"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 并不是所有加入购物车的人都进行了收藏，有29%的人并没有收藏\n",
    "(data_user_count[2] - data_user_count[1])/data_user_count[2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.185128400Z",
     "start_time": "2024-05-07T08:38:56.141513700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "0.5044237207077953"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 收藏后，有50%进行了支付\n",
    "(data_user_count[1] - data_user_count[3])/data_user_count[1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.186836500Z",
     "start_time": "2024-05-07T08:38:56.155848300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "0.6501234122317822"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加入购物车后，65%没有进行支付\n",
    "(data_user_count[2] - data_user_count[3])/data_user_count[2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:39:49.617811100Z",
     "start_time": "2024-05-07T08:39:49.563766800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "96.09068674347989"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多少次点击才有一次支付\n",
    "data_user_count[0]/data_user_count[3]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.196411100Z",
     "start_time": "2024-05-07T08:38:56.186633500Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# RFM 分析"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.284387200Z",
     "start_time": "2024-05-07T08:38:56.197409600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "Timestamp('2014-12-18 23:00:00')"
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.time.max()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.323718100Z",
     "start_time": "2024-05-07T08:38:56.213591100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "Timestamp('2014-12-18 00:00:00')"
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.date.max()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.323718100Z",
     "start_time": "2024-05-07T08:38:56.257221800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n12256901  132653097  119946062             2          NaN          6054   \n12256902  130082553  296196819             1          NaN         11532   \n12256903   43592945  350594832             1      9rhhgph          9541   \n12256904   12833799  186993938             1      954g37v          3798   \n12256905   77522552   69292191             1          NaN           889   \n\n                        time       date  hour  operation  \n12256901 2014-12-18 23:00:00 2014-12-18    23          1  \n12256902 2014-12-18 23:00:00 2014-12-18    23          1  \n12256903 2014-12-18 23:00:00 2014-12-18    23          1  \n12256904 2014-12-18 23:00:00 2014-12-18    23          1  \n12256905 2014-12-18 23:00:00 2014-12-18    23          1  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n      <th>operation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12256901</th>\n      <td>132653097</td>\n      <td>119946062</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>6054</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12256902</th>\n      <td>130082553</td>\n      <td>296196819</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>11532</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12256903</th>\n      <td>43592945</td>\n      <td>350594832</td>\n      <td>1</td>\n      <td>9rhhgph</td>\n      <td>9541</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12256904</th>\n      <td>12833799</td>\n      <td>186993938</td>\n      <td>1</td>\n      <td>954g37v</td>\n      <td>3798</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12256905</th>\n      <td>77522552</td>\n      <td>69292191</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>889</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:56.378057200Z",
     "start_time": "2024-05-07T08:38:56.312787400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### R：每个用户的最近一次购买时间"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [],
   "source": [
    "# sort_values默认升序，然后取最后一个就是最近的\n",
    "# 每个用户最新的购买日期和参考日期之间的时间间隔，即最后一次购买时间\n",
    "recent_buy_time=data_user[data_user['behavior_type'] == '4'].groupby ('user_id')['date'].apply(lambda x:datetime(2014,12,18)- x.sort_values().iloc[-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:58.659660700Z",
     "start_time": "2024-05-07T08:38:56.315004300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id\n100001878    0 days\n100011562    2 days\n100012968    0 days\n100014060    0 days\n100024529    2 days\n              ...  \n99960313     5 days\n9996155     11 days\n99963140     3 days\n99968428     4 days\n99989881     3 days\nName: date, Length: 8886, dtype: timedelta64[ns]"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_buy_time"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:58.674874500Z",
     "start_time": "2024-05-07T08:38:58.665646200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id recent\n0  100001878 0 days\n1  100011562 2 days\n2  100012968 0 days\n3  100014060 0 days\n4  100024529 2 days",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>recent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100001878</td>\n      <td>0 days</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100011562</td>\n      <td>2 days</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100012968</td>\n      <td>0 days</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100014060</td>\n      <td>0 days</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100024529</td>\n      <td>2 days</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_buy_time=recent_buy_time.reset_index().rename(columns={'date':'recent'})\n",
    "recent_buy_time.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:58.733376900Z",
     "start_time": "2024-05-07T08:38:58.679222900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "        user_id  recent\n0     100001878       0\n1     100011562       2\n2     100012968       0\n3     100014060       0\n4     100024529       2\n...         ...     ...\n8881   99960313       5\n8882    9996155      11\n8883   99963140       3\n8884   99968428       4\n8885   99989881       3\n\n[8886 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>recent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100001878</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100011562</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100012968</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100014060</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100024529</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8881</th>\n      <td>99960313</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>8882</th>\n      <td>9996155</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>8883</th>\n      <td>99963140</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8884</th>\n      <td>99968428</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>8885</th>\n      <td>99989881</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>8886 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉days\n",
    "recent_buy_time['recent']=recent_buy_time.recent.apply(lambda x: x.days)\n",
    "recent_buy_time"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:58.783617400Z",
     "start_time": "2024-05-07T08:38:58.688465Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id    object\nrecent      int64\ndtype: object"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_buy_time.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:58.783617400Z",
     "start_time": "2024-05-07T08:38:58.723029300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "            recent\ncount  8886.000000\nmean      5.811839\nstd       6.678478\nmin       0.000000\n25%       1.000000\n50%       4.000000\n75%       7.000000\nmax      30.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>recent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>8886.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.811839</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>6.678478</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用户的平均最近购买时间为5.8天前\n",
    "recent_buy_time.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:58.816543100Z",
     "start_time": "2024-05-07T08:38:58.738303Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### F：每个用户在这个月有多少天购买"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id\n100001878    15\n100011562     3\n100012968    11\n100014060    12\n100024529     9\n             ..\n99960313      5\n9996155       3\n99963140      8\n99968428      9\n99989881     13\nName: date, Length: 8886, dtype: int64"
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count和nunique的区别,count是计算一个月总的购物次数，nunique是计算唯一值的个数（有多少天购物）\n",
    "buy_freq = data_user[data_user.behavior_type =='4'].groupby('user_id').date.nunique()\n",
    "\n",
    "buy_freq"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.670499300Z",
     "start_time": "2024-05-07T08:38:59.471737100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "data": {
      "text/plain": "        user_id  freq\n0     100001878    15\n1     100011562     3\n2     100012968    11\n3     100014060    12\n4     100024529     9\n...         ...   ...\n8881   99960313     5\n8882    9996155     3\n8883   99963140     8\n8884   99968428     9\n8885   99989881    13\n\n[8886 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>freq</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100001878</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100011562</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100012968</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100014060</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100024529</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8881</th>\n      <td>99960313</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>8882</th>\n      <td>9996155</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8883</th>\n      <td>99963140</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>8884</th>\n      <td>99968428</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>8885</th>\n      <td>99989881</td>\n      <td>13</td>\n    </tr>\n  </tbody>\n</table>\n<p>8886 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy_freq=buy_freq.reset_index().rename(columns={'date':'freq'})\n",
    "buy_freq"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.713444400Z",
     "start_time": "2024-05-07T08:38:59.671746Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [],
   "source": [
    "rfm= pd.merge (recent_buy_time, buy_freq,left_on ='user_id' ,right_on ='user_id')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.799585300Z",
     "start_time": "2024-05-07T08:38:59.698979Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "data": {
      "text/plain": "        user_id  recent  freq\n0     100001878       0    15\n1     100011562       2     3\n2     100012968       0    11\n3     100014060       0    12\n4     100024529       2     9\n...         ...     ...   ...\n8881   99960313       5     5\n8882    9996155      11     3\n8883   99963140       3     8\n8884   99968428       4     9\n8885   99989881       3    13\n\n[8886 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>recent</th>\n      <th>freq</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100001878</td>\n      <td>0</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100011562</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100012968</td>\n      <td>0</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100014060</td>\n      <td>0</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100024529</td>\n      <td>2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8881</th>\n      <td>99960313</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>8882</th>\n      <td>9996155</td>\n      <td>11</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8883</th>\n      <td>99963140</td>\n      <td>3</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>8884</th>\n      <td>99968428</td>\n      <td>4</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>8885</th>\n      <td>99989881</td>\n      <td>3</td>\n      <td>13</td>\n    </tr>\n  </tbody>\n</table>\n<p>8886 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.844385900Z",
     "start_time": "2024-05-07T08:38:59.727392100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [],
   "source": [
    "rfm['recent_value'] = pd.qcut(rfm.recent,2,labels=['2','1']) # qcut就是自动划分，2是分成两组"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.844385900Z",
     "start_time": "2024-05-07T08:38:59.764587800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [],
   "source": [
    "rfm['freq_value'] = pd.qcut(rfm.freq,2,labels=['1','2'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.844385900Z",
     "start_time": "2024-05-07T08:38:59.773566500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [],
   "source": [
    "rfm['rfm'] = rfm['recent_value'].str.cat(rfm['freq_value'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.845384100Z",
     "start_time": "2024-05-07T08:38:59.789240800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "      user_id  recent  freq recent_value freq_value rfm\n0   100001878       0    15            2          2  22\n1   100011562       2     3            2          1  21\n2   100012968       0    11            2          2  22\n3   100014060       0    12            2          2  22\n4   100024529       2     9            2          2  22\n..        ...     ...   ...          ...        ...  ..\n95  101050024       3    11            2          2  22\n96  101061002       1    11            2          2  22\n97  101120745       4     7            2          2  22\n98  101122114       6     6            1          2  12\n99  101150402       6     8            1          2  12\n\n[100 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>recent</th>\n      <th>freq</th>\n      <th>recent_value</th>\n      <th>freq_value</th>\n      <th>rfm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100001878</td>\n      <td>0</td>\n      <td>15</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100011562</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100012968</td>\n      <td>0</td>\n      <td>11</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100014060</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100024529</td>\n      <td>2</td>\n      <td>9</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>101050024</td>\n      <td>3</td>\n      <td>11</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>101061002</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>101120745</td>\n      <td>4</td>\n      <td>7</td>\n      <td>2</td>\n      <td>2</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>101122114</td>\n      <td>6</td>\n      <td>6</td>\n      <td>1</td>\n      <td>2</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>101150402</td>\n      <td>6</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head(100)  #22是最佳"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.846383Z",
     "start_time": "2024-05-07T08:38:59.805309700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "        user_id  recent  freq recent_value freq_value rfm\n7     100039408      29     1            1          1  11\n9      10004287      18     3            1          1  11\n13    100090063       6     2            1          1  11\n15    100109755       5     5            1          1  11\n18    100121842       6     2            1          1  11\n...         ...     ...   ...          ...        ...  ..\n8865   99775965       6     2            1          1  11\n8866   99781075      18     2            1          1  11\n8876   99856680       6     2            1          1  11\n8881   99960313       5     5            1          1  11\n8882    9996155      11     3            1          1  11\n\n[3195 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>recent</th>\n      <th>freq</th>\n      <th>recent_value</th>\n      <th>freq_value</th>\n      <th>rfm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7</th>\n      <td>100039408</td>\n      <td>29</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10004287</td>\n      <td>18</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>100090063</td>\n      <td>6</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>100109755</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>100121842</td>\n      <td>6</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8865</th>\n      <td>99775965</td>\n      <td>6</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>8866</th>\n      <td>99781075</td>\n      <td>18</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>8876</th>\n      <td>99856680</td>\n      <td>6</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>8881</th>\n      <td>99960313</td>\n      <td>5</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>8882</th>\n      <td>9996155</td>\n      <td>11</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n<p>3195 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[rfm['rfm']=='11']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T08:38:59.868642Z",
     "start_time": "2024-05-07T08:38:59.830596300Z"
    }
   }
  }
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